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Research 2020年第1期2020卷 522-533页

作者： asim Riaz muhammad Umair Ali T.Gabriel Enge Takuya Tsuzuki Adrian Lowe Wojciech Lipinski Research School of Electrical Energy and Materials EngineeringThe Australian National UniversityCanberra ACT 2601Australia Department of Materials Science and Engineering College of EngineeringPeking UniversityBeijing 100871China Research School of Earth Sciences The Australian National UniversityCanberra ACT 2601Australia

The effects of V and Ce concentrations(each varying in the 0-100%range)in vanadia-ceria multiphase systems are investigated for synthesis gas production via thermochemical redox cycles of CO_(2) and H_(2)O splitting coupled to methane partial oxidation *** oxidation of prepared oxygen carriers is performed by separate and sequential CO_(2) and H_(2)O splitting *** and chemical analyses of the mixed-metal oxides revealed important information about the Ce and V interactions affecting their crystal phases and redox *** CeO_(2) and pure V_(2)O_(5) are found to offer the lowest and highest oxygen exchange capacities and syngas production performance,*** mixed-oxide systems provide a balanced performance:their oxygen exchange capacity is up to 5 times higher than that of pure CeO_(2) while decreasing the extent of methane *** addition of 25%V to CeO_(2) results in an optimum mixture of CeO_(2) and CeVO4 for enhanced CO_(2) and H_(2)O *** higher V concentrations,cyclic carbide formation and oxidation result in a syngas yield higher than that for pure CeO_(2).

关键词： pure methane splitting

Drug Response Prediction of Liver Cancer Cell Line Using Deep Learning

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Computers, Materials & Continua 2022年第2期70卷 2743-2760页

作者： Mehdi Hassan Safdar Ali muhammad Sanaullah Khuram Shahzad Sadaf mushtaq Rashda Abbasi Zulqurnain Ali Hani Alquhayz Department of Computer Science Air UniversityIslamabad44000Pakistan Directorate General National Repository Islamabad44000Pakistan Department of Computer Science Bahauddin Zakariya UniversityMultan60000Pakistan Department of Physics Air UniversityIslamabad44000Pakistan Department of Biotechnology Quaid-i-Azam University Islamabad44000Pakistan Institute of Biomedical and Genetic Engineering Islamabad44000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Cancer is the second deadliest human disease worldwide with high mortality *** and treatment of this disease requires precise and automatic assessment of effective drug response and control *** of treated and untreated cancerous cell line is one of the most challenging problems for precise and targeted drug delivery and response.A novel approach is proposed for prediction of drug treated and untreated cancer cell line automatically by employing modified Deep neural *** hepatocellular carcinoma(HepG2)cells are exposed to anticancer drug functionalized CFO@BTO nanoparticles developed by our *** models are developed by modifying ResNet101 and exploiting the transfer learning *** three layers of ResNet101 are re-trained for the identification of drug treated cancer *** learning approach in an appropriate choice especially when there is limited amount of annotated *** proposed technique is validated on acquired 203 fluorescentmicroscopy images of human HepG2 cells treated with drug functionalized cobalt ferrite@barium titanate(CFO@BTO)magnetoelectric nanoparticles in *** developed approach achieved high prediction with accuracy of 97.5%and sensitivity of 100%and outperformed other *** high performance reveals the effectiveness of the *** is scalable and fully automatic prediction approach which can be extended for other similar cell diseases such as lung,brain tumor and breast cancer.

关键词： Drug delivery in vitro transfer learning microscopic images deep learning

A Concise and Varied Visual Features-Based Image Captioning Model with Visual Selection

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Computers, Materials & Continua 2024年第11期81卷 2873-2894页

作者： Alaa Thobhani Beiji Zou Xiaoyan Kui Amr Abdussalam muhammad asim Naveed Ahmed Mohammed Ali Alshara School of Computer Science and Engineering Central South UniversityChangsha410083China Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic UniversityRiyadh11432Saudi Arabia

Image captioning has gained increasing attention in recent *** characteristics found in input images play a crucial role in generating high-quality *** studies have used visual attention mechanisms to dynamically focus on localized regions of the input image,improving the effectiveness of identifying relevant image regions at each step of caption ***,providing image captioning models with the capability of selecting the most relevant visual features from the input image and attending to them can significantly improve the utilization of these ***,this leads to enhanced captioning network *** light of this,we present an image captioning framework that efficiently exploits the extracted representations of the *** framework comprises three key components:the Visual Feature Detector module(VFD),the Visual Feature Visual Attention module(VFVA),and the language *** VFD module is responsible for detecting a subset of the most pertinent features from the local visual features,creating an updated visual features ***,the VFVA directs its attention to the visual features matrix generated by the VFD,resulting in an updated context vector employed by the language model to generate an informative *** the VFD and VFVA modules introduces an additional layer of processing for the visual features,thereby contributing to enhancing the image captioning model’s *** the MS-COCO dataset,our experiments show that the proposed framework competes well with state-of-the-art methods,effectively leveraging visual representations to improve *** implementation code can be found here:https://***/althobhani/VFDICM(accessed on 30 July 2024).

关键词： Visual attention image captioning visual feature detector visual feature visual attention

AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis

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Computers, Materials & Continua 2024年第11期81卷 2077-2131页

作者： Mohd Asif Hajam Tasleem Arif Akib Mohi Ud Din Khanday Mudasir Ahmad Wani muhammad asim Department of Information Technology Baba Ghulam Shah Badshah UniversityRajouriJammu and Kashmir185234India Department of Computer Science Samarkand International University of TechnologySamarkand141500Uzbekistan EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern *** the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant *** images,however,stand out as the preferred and easily accessible source of *** plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human *** intelligence(AI)techniques offer a solution by automating plant recognition *** study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned *** paper critically summarizes relevant literature based on AI algorithms,extracted features,and results ***,it analyzes extensively used datasets in automated plant classification *** also offers deep insights into implemented techniques and methods employed for medicinal plant ***,this rigorous review study discusses opportunities and challenges in employing these AI-based ***,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research *** review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.

关键词： Pattern recognition artificial intelligence machine learning deep learning image processing plant leaf identification

Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

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Computers, Materials & Continua 2023年第6期75卷 5379-5393页

作者： Abida Sharif Imran Sharif muhammad asim Saleem muhammad Attique Khan Majed Alhaisoni Marriam Nawaz Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadPakistan Faculty of Computing Riphah International UniversityFaisalabadPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science UET TaxilaTaxilaPakistan Department of Software Engineering UET TaxilaTaxilaPakistan Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942KSA Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a *** a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid ***,optimal path selection to route traffic between the origin and destination is *** research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network ***,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal *** the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal *** model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective ***,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.

关键词： Internet of vehicles internet of things fuzzy logic optimization path planning

Phytochemical analysis and inhibitory effects of Calligonum polygonoides on pancreatic α-amylase and β-glucosidase enzymes

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Journal of Traditional Chinese Medicine 2022年第3期42卷 426-431页

作者： mushtaq Ahmed Naila Sher Nadia mushtaq Rahmat Ali Khan Department of Biotechnology Faculty of Biological SciencesUniversity of Science&TechnologyBannu 28100KPK-Pakistan Department of Botany Faculty of Biological SciencesUniversity of Science&TechnologyBannu 28100KPKPakistan

OBJECTIVES:To estimate the existence of phytochemicals and then to determine the antidiabetic activity againstα-amylase andβ-glucosidase inhibition in ***:The study was carried out by following standard ***:Phytochemicals analysis indicated the presence of different *** total phenolic content was 6.055 mg GAE/g and the total flavonoid content was 5.706 mg RU/g in the plant *** total saponins,alkaloids,and tannins contents were(0.044%),(2.88%)and(2.862 nm)respectively.α-amylase inhibition activity of Calligonum polygonoides(CP)extract was 70%with IC50 of 610μg/mL and that ofβ-glucosidase inhibition activity was 65%with IC50 of 640μg/***:The findings reported for the first time the antidiabetes-promoting effects of an extract of CP in vitro,thus validating their promising anti-diabetes potential.

关键词： Calligonum polygonoides phytochemicals alpha-amylases beta-glucosidase

A Machine Learning Approach for Expression Detection in Healthcare Monitoring Systems

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Computers, Materials & Continua 2021年第5期67卷 2123-2139页

作者： muhammad Kashif Ayyaz Hussain asim Munir Abdul Basit Siddiqui AaqifAfzaal Abbasi muhammad Aakif Arif Jamal Malik Fayez Eid Alazemi Oh-Young Song Department of Computer Science&Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science Quaid-i-Azam UniversityIslamabad44000Pakistan Department of Computer Science Capital University of Science&TechnologyIslamabad44000Pakistan Department of Software Engineering Foundation UniversityIslamabad44000Pakistan Department of Computer Science Abdul Wali Khan UniversityMardan23200Pakistan Department of Computer Science and Information Systems College of Business StudiesPAAET12062Kuwait Department of Software Sejong UniversitySeoul05006Korea

Expression detection plays a vital role to determine the patient’s condition in healthcare *** helps the monitoring teams to respond swiftly in case of *** to the lack of suitable methods,results are often compromised in an unconstrained environment because of pose,scale,occlusion and illumination variations in the image of the face of the patient.A novel patch-based multiple local binary patterns(LBP)feature extraction technique is proposed for analyzing human behavior using facial expression *** consists of three-patch[TPLBP]and four-patch LBPs[FPLBP]based feature engineering *** representation is encoded from local patch statistics using these *** and FPLBP capture information that is encoded to find likenesses between adjacent patches of pixels by using short bit strings contrary to pixel-based *** images are transformed into the frequency domain using a discrete cosine transform(DCT).Most discriminant features extracted from coded DCT images are combined to generate a feature *** vector machine(SVM),k-nearest neighbor(KNN),and Naïve Bayes(NB)are used for the classification of facial expressions using selected *** experimentation is performed to analyze human behavior by considering standard extended Cohn Kanade(CK+)and Oulu–CASIA *** demonstrate that the proposed methodology outperforms the other techniques used for comparison.

关键词： Detection expressions gestures analytics pain patch-based local binary descriptor discrete cosine transform healthcare

Detecting and Mitigating DDOS Attacks in SDNs Using Deep Neural Network

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Computers, Materials & Continua 2023年第11期77卷 2157-2178页

作者： Gul Nawaz muhammad Junaid Adnan Akhunzada Abdullah Gani Shamyla Nawazish asim Yaqub Adeel Ahmed Huma Ajab Department of Information Technology The University of HaripurHaripur22060Pakistan Faculty of Computing and Informatics University Malaysia SabahSabah88400Malaysia Department of Environmental Sciences COMSATS University Abbottabad CampusAbbottabad22010Pakistan Department of Chemistry COMSATS University Abbottabad CampusAbbottabad22010Pakistan

Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate *** proposed a deep neural network(DNN)model for the detection of DDoS attacks in the Software-Defined Networking(SDN)*** centralizes the control plane and separates it from the data *** simplifies a network and eliminates vendor specification of a *** of this open nature and centralized control,SDN can easily become a victim of DDoS *** proposed a supervised Developed Deep Neural Network(DDNN)model that can classify the DDoS attack traffic and legitimate *** Developed Deep Neural Network(DDNN)model takes a large number of feature values as compared to previously proposed Machine Learning(ML)*** proposed DNN model scans the data to find the correlated features and delivers high-quality *** model enhances the security of SDN and has better accuracy as compared to previously proposed *** choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing *** model results in a high accuracy rate of 99.76%with a low false-positive rate and 0.065%low loss *** accuracy increases to 99.80%as we increase the number of epochs to 100 *** proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed *** can handle a huge amount of structured and unstructured data and can easily solve complex problems.

关键词： Distributed denial of service(DDoS)attacks software-defined networking(SDN) classification deep neural network(DNN)

Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

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Computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： muhammad Armghan Latif Zohaib mushtaq Saad Arif Sara Rehman muhammad Farrukh Qureshi Nagwan Abdel Samee Maali Alabdulhafith Yeong Hyeon Gu Mohammed A.Al-masni Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid *** and timely diagnosis of these disorders is crucial for effective treatment and patient *** research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection *** forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this *** ensemble learning,random forest,adaptive boosting,and bagging classifiers are *** effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature *** experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,*** enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical *** significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed *** research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

Novel LaFe_(2)O_(4)spinel structure with a large oxygen reduction response towards protonic ceramic fuel cell cathode

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Journal of Rare Earths 2023年第3期41卷 413-421,I0004页

作者： Jinping Wang Yuzheng Lu Naveed mushtaq M.A.K Yousaf Shah Sajid Rauf Peter D.Lund muhammad Imran Asghar School of Energy and Power Engineering Nanjing Institute of TechnologyNanjing 211167China School of Electronic Engineering Nanjing Xiao Zhuang UniversityNanjing 211171China Jiangsu Provincial Key Laboratory of Solar Energy Science and Technology/Energy Storage Joint Research Center School of Energy and EnvironmentSoutheast UniversityNanjing 210096China College of Electronics and Information Engineering Shenzhen UniversityShenzhen 518000China New Energy Technologies Group Department of Applied PhysicsAalto University School of ScienceP.O.Box.15100FI00076AaltoEspooFinland Hubei Collaborative Innovation Center for Advanced Organic Chemical Materials Faculty of Physics and Electronic ScienceHubei UniversityWuhan 430062China

Highly active and stable electrocatalysts are mandatory for developing high-performance and longlasting fuel *** current study demonstrates a high oxygen reduction reaction(ORR)electrocatalytic activity of a novel spinel-structured LaFe_(2)O_(4)via a self-doping *** LaFe_(2)O_(4)demonstrates excellent ORR activity in a protonic ceramic fuel cell(PCFC)at temperature range of 350-500℃.The high ORR activity of LaFe_(2)O_(4)is mainly attributed to the facile release of oxide and proton ions,and improved synergistic incorporation abilities associated with interplay of multivalent Fe^(3+)/Fe^(2+)and La^(3+)*** LaFe_(2)O_(4)as cathode over proton conducting BaZr_(0.4)Ce_(0.4)Y_(0.2)O_(3)(BZCY)electrolyte,the fuel cell has delivered a high-power density of 806 mW/cm^(2)operating at 500℃.Different spectroscopic and calculations methods such as UV-visible,Raman,X-ray photoelectron spectroscopy and density functional theory(DFT)calculations were performed to screen the potential application of LaFe_(2)O_(4)as *** study would help in developing functional cobalt-free ORR electrocatalysts for low temperature-PCFCs(LT-PCFCs)and solid oxide fuel cells(SOFCs)applications.

关键词： LaFe_(2)O_(4)cathode Facile Fe^(3+/2+)ions resale Oxygen reductionreaction(ORR) Excellent electrocatalyst Protonic ceramicfuel cell(PCFC) Rare earths